In Supervised Learning method, train the model with the known input and output data which predicts the future results.

In Unsupervised Learning method, train the model using the information that is neither classified and allow the algorithm to act on the information without any guidance.

Each learning method has sub-categories as mentioned below.

Supervised Learning

Classification

Classification learning method is used for categorising a certain observation into a group.

For ex:

A simple use case would be, to predict if a given email is spam or not?

Classifying consumers reasons of visit in store in order to send them a personalized campaign.

Classification Algorithms

Discriminant analysis

K-nearest neighbor

Support Vector Machine (SVM)

Boosted decision trees

Bagged decision trees

Regression

Regressing learning method is used for predicting and forecasting for continuous values.

For ex:

Predicting a heart attack based on data from an electro cardiogram

Regression Algorithms

Linear model

Nonlinear model

Regularization

Stepwise regression

Unsupervised Learning

Clustering

Clustering is the process of grouping similar entities together. The goal of this unsupervised machine learning technique is to find similarities in the data point and group similar data points together.

For Ex:

You can identify different groups/segments of customers and market each group in a different way to maximize the revenue.